thoughts from the intersection of science, technology and politics

I was asked to put together a thought piece for Wired Magazine’s “The world in 2015″ annual edition. This gave me the opportunity to think about some of the big forces that are going to shape this coming year. I decided to write about robot propaganda, aka what happens when we get so good at determining what people want to hear that we can employ propaganda bots to do this for us. You can read the full article below, or here’s the pdf of the original.

ROBOT PROPAGANDA

Artificial intelligence and learning algorithms will make it almost impossible to tell robots from humans – and real news from fake. So what’s on the agenda?

HUMANITY HAS BEEN ADVANCING the field of propaganda for as long as we’ve been at war or had political fights to win. But today, propaganda is undergoing a significant change based on the latest advances in the fields of big data and artificial intelligence. Over the past decade, billions of dollars have been invested in technologies that customise ads increasingly precisely based on individuals’ preferences. Now this is making the jump to the world of politics and the manipulation of ideas.

Some recent military experiments in computational propaganda indicate where this could be taking us. In 2008, the US State Department, through its “foreign assistance” agency USAID, set up a fake social network in Cuba. Supposedly concerned with public health and civics, it’s operatives actively targeted likely dissidents. The site came complete with hashtags, dummy advertisements and a database of users’ “political tendencies”. For an estimated $1.6m (£1m), USAID was, between 2009 and 2012, able to control a major information platform in Cuba with potential to influence the spread of ideas among 40,000 unique profiles. Building on this project in 2011, USCENTCOM (United States Central Command) – the US military force responsible for operations in the broader Middle East region – awarded a contract to a Californian firm to build an “online persona management service”, complete with fake online profiles that have convincing backgrounds and histories. The software will allow US service personnel to operate up to ten separate false identities based all over the world from their workstations “without fear of being discovered by sophisticated adversaries”. These personas allow the military to recruit, spy on and manipulate peoples’ behaviour and ideas.

Such projects represent the first wave of computational propaganda, but they are constrained in their scale (and ultimately their effectiveness) by the simple fact that each profile has to be driven by an actual human on the other side. In 2015, we will see the emergence of more automated computational propaganda – bots using sophisticated artificial intelligence frameworks, removing the need to have humans operate the profiles. Algorithms will not only read the news, but write it.

One of the fun things about doing the kind of research that I do is that sometimes Morgan Freeman** shows up with a Television crew in tow. Earlier this year I was lucky enough to be able to put together a show with him about some of the more cutting edge ideas of free will and determinism. The idea being that although we may have some freedom when it comes to an individual within society, once you scale these kinds of systems up towards millions the aggregate behavior starts to become quite predictable. While freedom may exist for an individual, do we indeed lose it as a society?

This question is particularly interesting when we start to consider the mathematical structures that appear time and again within insurgencies and war. From the timing of attacks through to the size distribution of attacks. The activities start to become ordered and predictable. When this order emerges do we lose some sense of free will? If even the way we choose to kill each other within a seemingly chaotic conflict is determined by a set of mathematical equations what does that say about our ability to collectively have free will?

Of course this is all nothing new to the Issac Assimov fans out there, and the fictional Professor Hari Seldon was/is a large proponent of galactic scale predictability with his version of psychohistory. With the emergence of global scale data collection and high performance computing systems available on demand – are we finally starting to brush up against this strange/scary concept?

I’m not sure I did quite enough Philosophy to answer this or any of the other challenging questions definitively. But it was certainly an interesting set of questions to ponder. It was a blast working with the ‘Through the Wormhole’ team, and great to be able to tell the story of the work I have been doing into understanding the mathematical structure of war and insurgency. And judging by the video you can see that is was also a great excuse to lace up the running spikes, get onto the track and get out over some hurdles — judging by the technique it’s been a while since I had this chance. You can watch some of the other clips from the show here including an interesting piece from Michael Gazzaniga aka the father of modern neuroscience.

**And because I know you are asking, I did of course get Morgan Freeman to record for me a voice message on my answer phone. Is there any other reason to work with him?

Late last year I was sitting in Osaka in a little bar in Dotonbori, watching the presidential election results come through. The television was tuned to the Japanese national broadcaster NHK and from time to time different results would come through. Obama kept piling up wins across the country until at around 1:30pm Japanese time CNN called the election for Obama. This result, like most of the earlier Electoral College results, was met with a muted response. A mild round of polite applause and then back to eating, drinking and text messaging friends. The relative indifference to the result couldn’t have been further from the hype my friends were experiencing first hand as they watched in New York and San Francisco. But as I watched the results my emotional levels mirrored that of the Japanese salaryman next to me in the bar – I felt very little in the way of anticipation or surprise. I had followed Nate Silver’s models over the past 6 months and as the Election Day drew closer and closer the models became more and more accurate. Checking in to the website fivethirtyeight.com on the Friday before the election and it was clear from the data and models that Silver had built that Obama would have an estimated 86% chance of winning. As the results came through, the models proved to be correct — there was simply nothing to be surprised about. It was the political equivalent of being up by 6 runs at the top of the 9th at the Giants baseball game — time to go home and beat the traffic.

The truth is that we are getting very good at predicting things like elections and the closer we get to the election day, the more accurate the prediction we can make. The models that power these predictions have been becoming increasingly more complex over the past decade and for someone like Nate Silver this improvement process has involved thousands of hours of work to get to what could be considered a v3.0 of his election model. Add to this an exponential growth in available data and on demand distributed cloud computing engines and the predictive power of these models start to become impressive. Three days out from the presidential election in the United States we can predict the likelihood* of either candidate winning — we have the technology. Get over it. Predicting election results 72 hours out will be as routine** as predicting the weather — and just as with predicting the weather, we will get much better with it over time. It’s fair to say that with electoral prediction we are only at the beginning of this upward facing curve.

For those of us in the data science field, the ability to predict election results comes as no surprise. As a group we spend our days trying to predict everything from which ads a Facebook user will click on, to which way the stock market will move in the next 1/10th of a second to the perfect movie to watch on a Wednesday evening. Which is to say nothing of our collective efforts to predict the evolution of conflicts, the movement of people on a daily basis and the outbreak of new viruses. Over the last 10 years we have got very good at predicting human behavior. Over the next 10 years the rest of the world will realize the implications of this — and a lack of election surprises is one of them.

Every year the Cartography and Geographic Information Society holds a competition – the equivalent of the Academy Awards for maps — for the best map of the United States. While it’s often won by one of the major players in the mapping world, like the US Census bureau, in 2010 it was won by a one-man shop run by David Imus of Eugene Oregon.

Imus’s map differed not just in the scale of operation, but in the very way he went about constructing it. Traditional map-makers make use of algorithms to position labels, size towns and arrange points of interest, and they farm out the rest of the work to teams in India to manually fill in. While Imus’ map was constructed on a computer it didn’t use algorithms, leading to Imus toiling 6,000 hours, 7 days a week, for two years, obsessing over font types, state boundary colors and things like what symbol to use for airports. The little touches made the difference – the map was beautiful.

Imus, his map and his technique for producing it, seem like an anachronistic throw back to a 1950s world of exacto knives. But his approach is both an example of the future of information and a reminder of its past. As the world is under going an explosion of big data, it’s the multi-dimensional map interface that will play the key role for displaying connected intelligence. As Imus’ showed, producing these maps will require art and algorithms to come together.

Finding intelligence in Big Data

Much has been written about the proliferation of data over the past few years. Big data has been compared to the new oil, and like with oil, the environmental footprint of data is being felt with data warehouses now consuming around 2 percent of the electricity in the US.

But data in and of itself is useless — just a pile of 1′s and 0′s stacked together in server farms dotted across the nation. For data to actually be useful it needs to have algorithms run on top of it, and these algorithms need to lead to decisions. These decisions could be small — like which ad should I insert at the start of a YouTube video — or large such as should I insert 30,000 new troops into Iraq.

“Killing drug lords gets headlines, but complexity analysis suggests they are the wrong people to target to bring down a cartel”.

New Scientist put together an interesting piece called ‘Destroying drug cartels, the mathematical way’. The article focussed on the recent death of Lazcano the leader of the Mexican cartel Los Zetas and on how mathematics, modeling and complexity theory are being used to fight drug wars like these.

Mexican drug ecosystem

My research looked at similar drug fueled conflicts and we found that the mathematical signatures that defined the violence in places like Colombia looked very similar to the signatures in more traditional type insurgencies like Iraq. The way that drug cartels organise themselves, evolve and compete with each other is very similar to the dynamics seen within insurgent groups in places like Iraq and Afghanistan. This is because there are only a few effective ways to organise a force against a conventional army/paramilitary unit – and either the groups evolve to find this solution or they die trying. The upside of this though is that strategies employed in Iraq to break apart insurgencies can be used to inflict damage to the Mexican drug ecosystem.

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My colleague and coauthor Neil Johnson makes an appearance on the Russian cable channel RT. In the 6 minute video he talks about the results of the Nature paper and looks at how the model can be used to inform strategic decisions.